




'''simple docstring'''


import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def     SCREAMING_SNAKE_CASE							(   a_						:				List[Any]      ):
  # This defines a "chinese character" as anything in the CJK Unicode block:
  #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
  #
  # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
  # despite its name. The modern Korean Hangul alphabet is a different block,
  # as is Japanese Hiragana and Katakana. Those alphabets are used to write
  # space-separated words, so they are not treated specially and handled
  # like the all of the other languages.
  if (
      (cp >= 0X4e00 and cp <= 0X9fff)
      or (cp >= 0X3400 and cp <= 0X4dbf)  #
      or (cp >= 0X20000 and cp <= 0X2a6df)  #
      or (cp >= 0X2a700 and cp <= 0X2b73f)  #
      or (cp >= 0X2b740 and cp <= 0X2b81f)  #
      or (cp >= 0X2b820 and cp <= 0X2ceaf)  #
      or (cp >= 0Xf900 and cp <= 0Xfaff)
      or (cp >= 0X2f800 and cp <= 0X2fa1f)  #
  ):  #
    return True

  return False


def     SCREAMING_SNAKE_CASE							(   a_						:				str      ):
  # word like '180' or '身高' or '神'
  for char in word:
    __a								=       ord(a_      )
    if not _is_chinese_char(a_      ):
      return 0
  return 1


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]      ):
  __a								=       set()

  for token in tokens:
    __a								=       len(a_      ) > 1 and is_chinese(a_      )
    if chinese_word:
      word_set.add(a_      )
  __a								=       list(a_      )
  return word_list


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				set()      ):
  if not chinese_word_set:
    return bert_tokens
  __a								=       max([len(a_      ) for w in chinese_word_set]      )

  __a								=       bert_tokens
  __a						,							__a								=       0, len(a_      )
  while start < end:
    __a								=       True
    if is_chinese(bert_word[start]      ):
      __a								=       min(end - start					,       a_      )
      for i in range(a_					,       1					,       -1      ):
        __a								=       ''.join(bert_word[start : start + i]      )
        if whole_word in chinese_word_set:
          for j in range(start + 1					,       start + i      ):
            __a								=       '##' + bert_word[j]
          __a								=       start + i
          __a								=       False
          break
    if single_word:
      start += 1
  return bert_word


def     SCREAMING_SNAKE_CASE							(   a_						:				List[str]					,       a_						:				LTP					,       a_						:				BertTokenizer      ):
  __a								=       []

  for i in range(0					,       len(a_      )					,       100      ):
    __a								=       ltp_tokenizer.seg(lines[i : i + 100]      )[0]
    __a								=       [get_chinese_word(a_      ) for r in res]
    ltp_res.extend(a_      )
  assert len(a_      ) == len(a_      )

  __a								=       []
  for i in range(0					,       len(a_      )					,       100      ):
    __a								=       bert_tokenizer(lines[i : i + 100]					,       add_special_tokens=a_					,       truncation=a_					,       max_length=512      )
    bert_res.extend(res['input_ids']      )
  assert len(a_      ) == len(a_      )

  __a								=       []
  for input_ids, chinese_word in zip(a_					,       a_      ):
    __a								=       []
    for id in input_ids:
      __a								=       bert_tokenizer._convert_id_to_token(a_      )
      input_tokens.append(a_      )
    __a								=       add_sub_symbol(a_					,       a_      )
    __a								=       []
    # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
    for i, token in enumerate(a_      ):
      if token[:2] == "##":
        __a								=       token[2:]
        # save chinese tokens' pos
        if len(a_      ) == 1 and _is_chinese_char(ord(a_      )      ):
          ref_id.append(a_      )
    ref_ids.append(a_      )

  assert len(a_      ) == len(a_      )

  return ref_ids


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]      ):
  # For Chinese (Ro)Bert, the best result is from : RoBERTa-wwm-ext (https://github.com/ymcui/Chinese-BERT-wwm)
  # If we want to fine-tune these model, we have to use same tokenizer : LTP (https://github.com/HIT-SCIR/ltp)
  with open(args.file_name					,       'r'					,       encoding='utf-8'      ) as f:
    __a								=       f.readlines()
  __a								=       [line.strip() for line in data if len(a_      ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
  __a								=       LTP(args.ltp      )  # faster in GPU device
  __a								=       BertTokenizer.from_pretrained(args.bert      )

  __a								=       prepare_ref(a_					,       a_					,       a_      )

  with open(args.save_path					,       'w'					,       encoding='utf-8'      ) as f:
    __a								=       [json.dumps(a_      ) + '\n' for ref in ref_ids]
    f.writelines(a_      )


if __name__ == "__main__":
  UpperCAmelCase_					=   argparse.ArgumentParser(description="prepare_chinese_ref")
  parser.add_argument(
      "--file_name",
      type=str,
      default="./resources/chinese-demo.txt",
      help="file need process, same as training data in lm",
  )
  parser.add_argument(
      "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
  )
  parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
  parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

  UpperCAmelCase_					=   parser.parse_args()
  main(args)









'''simple docstring'''


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     SCREAMING_SNAKE_CASE							(   a_						:				str = ""      ):
  __a								=       url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
  __a								=       BeautifulSoup(requests.get(a_      ).text					,       'html.parser'      )
  __a								=       soup.find_all('td'					,       attrs='titleColumn'      )
  __a								=       soup.find_all('td'					,       class_='ratingColumn imdbRating'      )
  return {
      title.a.text: float(rating.strong.text      )
      for title, rating in zip(a_					,       a_      )
  }


def     SCREAMING_SNAKE_CASE							(   a_						:				str = "IMDb_Top_250_Movies.csv"      ):
  __a								=       get_imdb_top_aaa_movies()
  with open(a_					,       'w'					,       newline=''      ) as out_file:
    __a								=       csv.writer(a_      )
    writer.writerow(['Movie title', 'IMDb rating']      )
    for title, rating in movies.items():
      writer.writerow([title, rating]      )


if __name__ == "__main__":
  write_movies()



